Load data
Let’s load the data
mydata <- read_csv(here("data", "sim_data_hte_staggered.csv"))In this new dataset, there are 50 states, 15 of which are treated and 35 untreated (controls)
The intervention was implemented at different times as below:
- state.name[1:5] (“Alabama”, “Alaska”, “Arizona”, “Arkansas”, “California”) enacted policy in 2000
- state.name[6:10] (“Colorado”, “Connecticut”, “Delaware”, “Florida”, “Georgia”) enacted policy in 2003
- state.name[6:10] (“Hawaii”, “Idaho”, “Illinois”, “Indiana”, “Iowa”) enacted policy in 2006
Visualize the data
mydata <- read_csv(here("data", "sim_data_hte_staggered.csv"))Rows: 2500 Columns: 10
── Column specification ──────────────────────────────────────────────
Delimiter: ","
chr (1): state
dbl (9): state_num, year, treated, post, treatedpost, y, xit, xt, xi
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.p_load("panelView")
panelview(y ~ treatedpost, data = mydata,
index = c("state","year"),
pre.post = TRUE) 
panelview(y ~ treatedpost, data = mydata,
index = c("state","year"),
type = "outcome",
by.group = TRUE)Warning in panelview(y ~ treatedpost, data = mydata, index =
c("state", : option "by.cohort" is not allowed with "by.group = TRUE"
or "by.group.side = TRUE". Ignored.
#For all the data
mydata %>%
ggplot(aes(x=year, y=y, group=state)) +
annotate("rect", fill = "gray", alpha = 0.5,
xmin = 2000, xmax = Inf,
ymin = -Inf, ymax = Inf) +
labs(title = paste("Outcome by year"),
x = "Year",
y = "Outcome",
color = "Treatment") +
geom_line(aes(color=factor(treated)), linewidth=0.5) +
scale_color_discrete(labels=c("Control", "Treated")) +
geom_vline(xintercept = 2000, lty=2) +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5)) 
Analyze the data: Callaway and Sant’Anna
For more information you can read the paper at here The accompanying website also has a nice tutorial here
load package and create new variables
p_load(did)
mydata1 <- mydata %>%
mutate(first_treated = case_when( (state %in% state.name[1:5]) ~ 2000,
(state %in% state.name[6:10]) ~ 2003,
(state %in% state.name[11:15]) ~ 2006,
TRUE~0))
paged_table(mydata1)Group-time effects
group_time_effects <- att_gt( yname = "y",
tname = "year",
idname = "state_num",
gname = "first_treated",
xformla = ~ xit,
data = mydata1)Warning in pre_process_did(yname = yname, tname = tname, idname = idname, : Be aware that there are some small groups in your dataset.
Check groups: 2000,2003,2006.Warning in att_gt(yname = "y", tname = "year", idname = "state_num",
gname = "first_treated", : Not returning pre-test Wald statistic due
to singular covariance matrixsummary(group_time_effects)
Call:
att_gt(yname = "y", tname = "year", idname = "state_num", gname = "first_treated",
xformla = ~xit, data = mydata1)
Reference: Callaway, Brantly and Pedro H.C. Sant'Anna. "Difference-in-Differences with Multiple Time Periods." Journal of Econometrics, Vol. 225, No. 2, pp. 200-230, 2021. <https://doi.org/10.1016/j.jeconom.2020.12.001>, <https://arxiv.org/abs/1803.09015>
Group-Time Average Treatment Effects:
Group Time ATT(g,t) Std. Error [95% Simult. Conf. Band]
2000 1962 -0.2620 0.8269 -2.5129 1.9888
2000 1963 0.8596 0.3730 -0.1556 1.8748
2000 1964 -2.0749 0.5371 -3.5371 -0.6128 *
2000 1965 0.9764 0.6995 -0.9275 2.8804
2000 1966 0.1867 0.9726 -2.4609 2.8342
2000 1967 0.0187 0.4489 -1.2033 1.2406
2000 1968 -0.3948 0.4090 -1.5082 0.7186
2000 1969 0.6199 0.7058 -1.3011 2.5410
2000 1970 -0.4810 0.5940 -2.0979 1.1359
2000 1971 0.7035 0.4149 -0.4258 1.8328
2000 1972 -0.6007 0.6036 -2.2437 1.0423
2000 1973 0.6623 0.6060 -0.9874 2.3120
2000 1974 -0.6884 0.9811 -3.3588 1.9820
2000 1975 0.4010 0.9593 -2.2101 3.0121
2000 1976 0.3134 1.2877 -3.1918 3.8185
2000 1977 -0.5069 0.8205 -2.7403 1.7264
2000 1978 -0.2897 0.3399 -1.2149 0.6355
2000 1979 0.1919 0.3582 -0.7832 1.1670
2000 1980 0.9995 0.6259 -0.7041 2.7031
2000 1981 -1.9423 0.6615 -3.7428 -0.1418 *
2000 1982 1.0972 0.4387 -0.0969 2.2913
2000 1983 0.5662 0.4763 -0.7302 1.8625
2000 1984 -0.9455 0.5456 -2.4307 0.5397
2000 1985 0.7564 0.7012 -1.1524 2.6651
2000 1986 -0.3502 0.9931 -3.0533 2.3529
2000 1987 -0.0923 0.6150 -1.7662 1.5817
2000 1988 -0.3408 0.2803 -1.1039 0.4223
2000 1989 -0.4171 0.4604 -1.6704 0.8363
2000 1990 1.1116 0.7680 -0.9789 3.2021
2000 1991 0.3985 1.0880 -2.5630 3.3599
2000 1992 -0.6315 0.4790 -1.9353 0.6723
2000 1993 0.0359 0.5958 -1.5858 1.6576
2000 1994 -0.1197 0.6545 -1.9012 1.6618
2000 1995 -0.5594 0.5481 -2.0514 0.9326
2000 1996 0.2045 0.3393 -0.7190 1.1279
2000 1997 0.6682 0.5214 -0.7512 2.0876
2000 1998 -0.3049 0.7798 -2.4277 1.8178
2000 1999 -1.0091 0.4417 -2.2115 0.1932
2000 2000 49.9829 0.7695 47.8882 52.0775 *
2000 2001 51.2656 1.4634 47.2822 55.2489 *
2000 2002 51.5659 0.6990 49.6633 53.4686 *
2000 2003 52.3739 1.0660 49.4722 55.2757 *
2000 2004 51.2495 0.9365 48.7003 53.7986 *
2000 2005 50.6407 0.7161 48.6913 52.5900 *
2000 2006 51.0882 0.9555 48.4873 53.6891 *
2000 2007 51.6237 1.1483 48.4979 54.7495 *
2000 2008 49.6271 0.7467 47.5946 51.6596 *
2000 2009 52.7165 1.3259 49.1074 56.3256 *
2000 2010 51.4586 0.7028 49.5457 53.3715 *
2003 1962 0.0718 0.5709 -1.4822 1.6259
2003 1963 1.1817 0.3282 0.2883 2.0752 *
2003 1964 -1.5577 0.8583 -3.8940 0.7786
2003 1965 0.1479 1.0814 -2.7956 3.0914
2003 1966 0.5047 1.5451 -3.7009 4.7103
2003 1967 -0.6410 1.0631 -3.5348 2.2528
2003 1968 1.3154 0.9124 -1.1680 3.7989
2003 1969 -0.1430 0.5975 -1.7694 1.4833
2003 1970 -0.4499 0.9455 -3.0236 2.1238
2003 1971 0.7534 1.2155 -2.5551 4.0618
2003 1972 -1.0148 1.3316 -4.6394 2.6098
2003 1973 -0.7429 1.2307 -4.0928 2.6069
2003 1974 0.2819 0.7972 -1.8881 2.4520
2003 1975 0.8578 0.7315 -1.1334 2.8490
2003 1976 1.2746 0.7082 -0.6530 3.2022
2003 1977 -2.7111 0.9616 -5.3287 -0.0935 *
2003 1978 0.3345 1.0760 -2.5944 3.2635
2003 1979 0.7477 0.9895 -1.9459 3.4412
2003 1980 0.6420 0.6143 -1.0300 2.3141
2003 1981 -1.5449 1.1014 -4.5429 1.4532
2003 1982 1.6706 1.2734 -1.7955 5.1368
2003 1983 -0.5434 0.4890 -1.8743 0.7876
2003 1984 0.3988 0.6096 -1.2605 2.0581
2003 1985 0.3084 0.7628 -1.7678 2.3847
2003 1986 -1.0176 0.6640 -2.8250 0.7898
2003 1987 0.3036 0.8019 -1.8792 2.4863
2003 1988 0.2360 0.5391 -1.2315 1.7034
2003 1989 0.1681 0.6549 -1.6146 1.9507
2003 1990 -0.8382 0.5657 -2.3781 0.7017
2003 1991 1.0345 1.0654 -1.8655 3.9345
2003 1992 -1.5943 1.0524 -4.4590 1.2703
2003 1993 1.6480 0.7890 -0.4995 3.7956
2003 1994 -0.5525 0.5113 -1.9444 0.8394
2003 1995 0.7012 0.5531 -0.8045 2.2068
2003 1996 -0.9851 0.5083 -2.3686 0.3985
2003 1997 -0.6565 0.7520 -2.7035 1.3905
2003 1998 0.7634 0.3528 -0.1969 1.7237
2003 1999 -1.4632 0.4947 -2.8098 -0.1165 *
2003 2000 1.9567 0.3703 0.9487 2.9646 *
2003 2001 0.0256 1.0591 -2.8574 2.9086
2003 2002 0.0362 0.9513 -2.5533 2.6256
2003 2003 99.0085 0.6146 97.3356 100.6814 *
2003 2004 99.5673 0.8876 97.1512 101.9834 *
2003 2005 99.5363 0.5445 98.0541 101.0185 *
2003 2006 98.7809 0.7361 96.7773 100.7845 *
2003 2007 99.2121 0.8438 96.9153 101.5088 *
2003 2008 98.4466 0.9530 95.8526 101.0406 *
2003 2009 100.8014 0.9509 98.2131 103.3897 *
2003 2010 99.0418 0.6697 97.2188 100.8647 *
2006 1962 0.0511 0.2177 -0.5414 0.6436
2006 1963 0.6249 0.6308 -1.0921 2.3420
2006 1964 -2.7170 0.2802 -3.4798 -1.9542 *
2006 1965 1.6024 0.2632 0.8861 2.3187 *
2006 1966 1.0555 0.4946 -0.2908 2.4018
2006 1967 -2.4754 0.3925 -3.5438 -1.4070 *
2006 1968 2.5717 0.3041 1.7441 3.3994 *
2006 1969 -0.8535 0.4514 -2.0822 0.3751
2006 1970 -0.7874 0.2234 -1.3954 -0.1793 *
2006 1971 1.4289 0.3838 0.3842 2.4736 *
2006 1972 -1.8847 0.5490 -3.3790 -0.3904 *
2006 1973 1.4229 0.4569 0.1791 2.6667 *
2006 1974 -1.3698 0.7321 -3.3626 0.6229
2006 1975 1.2419 0.5846 -0.3494 2.8333
2006 1976 0.5061 0.5226 -0.9164 1.9286
2006 1977 -2.3132 0.6148 -3.9868 -0.6396 *
2006 1978 1.2382 0.2901 0.4484 2.0279 *
2006 1979 0.1057 0.7192 -1.8520 2.0633
2006 1980 0.0619 1.1893 -3.1754 3.2991
2006 1981 -0.8778 0.6247 -2.5783 0.8227
2006 1982 1.3548 0.3954 0.2786 2.4311 *
2006 1983 -0.3826 1.0080 -3.1262 2.3611
2006 1984 0.0961 0.6165 -1.5820 1.7742
2006 1985 0.8837 0.1912 0.3633 1.4041 *
2006 1986 -1.0373 0.7014 -2.9464 0.8718
2006 1987 -0.5428 0.7310 -2.5327 1.4470
2006 1988 0.5303 0.5056 -0.8459 1.9066
2006 1989 -0.3930 0.4141 -1.5201 0.7341
2006 1990 0.0164 0.4795 -1.2887 1.3216
2006 1991 1.2929 0.8039 -0.8953 3.4811
2006 1992 -1.4642 0.7989 -3.6389 0.7104
2006 1993 1.8158 0.4901 0.4817 3.1499 *
2006 1994 -3.1738 0.2983 -3.9858 -2.3619 *
2006 1995 2.2523 0.3320 1.3484 3.1561 *
2006 1996 -1.1757 0.5193 -2.5892 0.2379
2006 1997 0.6384 0.4716 -0.6454 1.9221
2006 1998 0.5867 0.5718 -0.9697 2.1431
2006 1999 -1.8927 0.4889 -3.2235 -0.5620 *
2006 2000 0.0785 0.8193 -2.1517 2.3087
2006 2001 3.4349 0.7325 1.4412 5.4286 *
2006 2002 -1.7928 1.1819 -5.0101 1.4244
2006 2003 0.9458 0.7738 -1.1606 3.0522
2006 2004 0.0706 0.6734 -1.7624 1.9035
2006 2005 -1.9094 0.3508 -2.8642 -0.9546 *
2006 2006 150.5473 0.4430 149.3415 151.7530 *
2006 2007 150.7607 0.4005 149.6706 151.8507 *
2006 2008 149.5590 0.6704 147.7342 151.3837 *
2006 2009 152.6349 0.5497 151.1386 154.1312 *
2006 2010 150.2910 0.2599 149.5834 150.9985 *
---
Signif. codes: `*' confidence band does not cover 0
Control Group: Never Treated, Anticipation Periods: 0
Estimation Method: Doubly Robustggdid(group_time_effects)
Simple Aggregation
agg.simple <- aggte(group_time_effects, type = "simple")
summary(agg.simple)
Call:
aggte(MP = group_time_effects, type = "simple")
Reference: Callaway, Brantly and Pedro H.C. Sant'Anna. "Difference-in-Differences with Multiple Time Periods." Journal of Econometrics, Vol. 225, No. 2, pp. 200-230, 2021. <https://doi.org/10.1016/j.jeconom.2020.12.001>, <https://arxiv.org/abs/1803.09015>
ATT Std. Error [ 95% Conf. Int.]
87.9908 10.2595 67.8825 108.0992 *
---
Signif. codes: `*' confidence band does not cover 0
Control Group: Never Treated, Anticipation Periods: 0
Estimation Method: Doubly RobustDynamic Effects and (Event Studies): Effect by length of exposure
agg.es <- aggte(group_time_effects, type = "dynamic")
summary(agg.es)
Call:
aggte(MP = group_time_effects, type = "dynamic")
Reference: Callaway, Brantly and Pedro H.C. Sant'Anna. "Difference-in-Differences with Multiple Time Periods." Journal of Econometrics, Vol. 225, No. 2, pp. 200-230, 2021. <https://doi.org/10.1016/j.jeconom.2020.12.001>, <https://arxiv.org/abs/1803.09015>
Overall summary of ATT's based on event-study/dynamic aggregation:
ATT Std. Error [ 95% Conf. Int.]
80.1577 6.444 67.5277 92.7878 *
Dynamic Effects:
Event time Estimate Std. Error [95% Simult. Conf. Band]
-44 0.0511 0.1915 -0.5054 0.6076
-43 0.6249 0.6024 -1.1254 2.3753
-42 -2.7170 0.3198 -3.6463 -1.7877 *
-41 0.8371 0.4315 -0.4167 2.0910
-40 1.1186 0.2864 0.2863 1.9509 *
-39 -2.0165 0.4767 -3.4017 -0.6314 *
-38 0.8192 0.5364 -0.7395 2.3779
-37 0.1703 0.5586 -1.4530 1.7935
-36 -1.1678 0.4437 -2.4572 0.1216
-35 1.2403 0.3625 0.1869 2.2936 *
-34 -0.6137 0.4347 -1.8768 0.6494
-33 0.3306 0.4590 -1.0031 1.6642
-32 -0.3371 0.5337 -1.8878 1.2137
-31 0.2823 0.5805 -1.4046 1.9693
-30 -0.2393 0.5462 -1.8263 1.3478
-29 -0.4426 0.5868 -2.1477 1.2625
-28 0.4984 0.3745 -0.5896 1.5865
-27 0.6809 0.3981 -0.4758 1.8375
-26 -1.1125 0.5865 -2.8167 0.5917
-25 -0.0474 0.5423 -1.6231 1.5282
-24 0.8053 0.5472 -0.7846 2.3952
-23 -0.0825 0.4320 -1.3378 1.1728
-22 -0.5795 0.3993 -1.7397 0.5807
-21 0.9154 0.4318 -0.3392 2.1700
-20 -0.1937 0.4228 -1.4224 1.0349
-19 -0.6954 0.4572 -2.0241 0.6332
-18 0.6453 0.3331 -0.3225 1.6131
-17 -0.2815 0.3048 -1.1672 0.6043
-16 -0.2085 0.3501 -1.2259 0.8089
-15 0.7617 0.3840 -0.3542 1.8777
-14 -0.5488 0.4767 -1.9339 0.8363
-13 0.2951 0.4023 -0.8739 1.4641
-12 -0.8267 0.5954 -2.5568 0.9034
-11 0.0803 0.5421 -1.4949 1.6555
-10 0.5280 0.5514 -1.0741 2.1301
-9 0.1615 0.3814 -0.9469 1.2698
-8 0.2188 0.3325 -0.7475 1.1851
-7 -0.9473 0.3252 -1.8923 -0.0023 *
-6 -0.2325 0.3993 -1.3928 0.9277
-5 1.2130 0.5469 -0.3763 2.8022
-4 -1.0172 0.4799 -2.4116 0.3772
-3 1.1902 0.3545 0.1601 2.2204 *
-2 -0.0696 0.4471 -1.3686 1.2295
-1 -0.9608 0.4410 -2.2422 0.3206
0 99.8462 10.0624 70.6075 129.0849 *
1 100.5312 10.1668 70.9891 130.0733 *
2 100.2204 9.8409 71.6254 128.8154 *
3 101.2632 10.2197 71.5675 130.9589 *
4 100.2508 9.9113 71.4511 129.0505 *
5 74.5436 7.2627 53.4401 95.6471 *
6 75.9448 7.8020 53.2741 98.6155 *
7 75.3328 7.1811 54.4665 96.1990 *
8 49.6271 0.7163 47.5457 51.7086 *
9 52.7165 1.2472 49.0925 56.3404 *
10 51.4586 0.7250 49.3520 53.5652 *
---
Signif. codes: `*' confidence band does not cover 0
Control Group: Never Treated, Anticipation Periods: 0
Estimation Method: Doubly Robustggdid(agg.es)
Group-Specific Effects: Effect by group
agg.gs <- aggte(group_time_effects, type = "group")
summary(agg.gs)
Call:
aggte(MP = group_time_effects, type = "group")
Reference: Callaway, Brantly and Pedro H.C. Sant'Anna. "Difference-in-Differences with Multiple Time Periods." Journal of Econometrics, Vol. 225, No. 2, pp. 200-230, 2021. <https://doi.org/10.1016/j.jeconom.2020.12.001>, <https://arxiv.org/abs/1803.09015>
Overall summary of ATT's based on group/cohort aggregation:
ATT Std. Error [ 95% Conf. Int.]
100.4312 0.3068 99.8299 101.0325 *
Group Effects:
Group Estimate Std. Error [95% Simult. Conf. Band]
2000 51.2357 0.7280 49.7656 52.7058 *
2003 99.2994 0.5344 98.2203 100.3784 *
2006 150.7585 0.3110 150.1306 151.3865 *
---
Signif. codes: `*' confidence band does not cover 0
Control Group: Never Treated, Anticipation Periods: 0
Estimation Method: Doubly Robustggdid(agg.gs)
# This is the group-specicf effects that can be used to estimate the overall ATT Calendar Time effects
agg.ct <- aggte(group_time_effects, type = "calendar")
summary(agg.ct)
Call:
aggte(MP = group_time_effects, type = "calendar")
Reference: Callaway, Brantly and Pedro H.C. Sant'Anna. "Difference-in-Differences with Multiple Time Periods." Journal of Econometrics, Vol. 225, No. 2, pp. 200-230, 2021. <https://doi.org/10.1016/j.jeconom.2020.12.001>, <https://arxiv.org/abs/1803.09015>
Overall summary of ATT's based on calendar time aggregation:
ATT Std. Error [ 95% Conf. Int.]
80.109 6.5566 67.2583 92.9597 *
Time Effects:
Time Estimate Std. Error [95% Simult. Conf. Band]
2000 49.9829 0.7327 48.1557 51.8100 *
2001 51.2656 1.3206 47.9722 54.5590 *
2002 51.5659 0.6718 49.8905 53.2413 *
2003 75.6912 7.3123 57.4549 93.9275 *
2004 75.4084 7.4427 56.8469 93.9699 *
2005 75.0885 7.4487 56.5122 93.6648 *
2006 100.1388 10.0195 75.1510 125.1265 *
2007 100.5322 10.0772 75.4005 125.6639 *
2008 99.2109 10.0486 74.1505 124.2713 *
2009 102.0509 10.2358 76.5238 127.5780 *
2010 100.2638 9.9858 75.3601 125.1675 *
---
Signif. codes: `*' confidence band does not cover 0
Control Group: Never Treated, Anticipation Periods: 0
Estimation Method: Doubly Robustggdid(agg.ct)
Analyze the data: Simple DID
Now that we have seen different effects that could be obtained using the method above, let us see what other models give us
p_load("estimatr")
dta <- lm_robust(y ~ treatedpost + factor(year) + xit,
data = mydata,
fixed_effects=state,
clusters = state,
se_type = "stata")
dta Estimate Std. Error t value Pr(>|t|)
treatedpost 87.325018 9.82403093 8.888919 8.636889e-12
factor(year)1962 3.805877 0.19858684 19.164797 2.066841e-24
factor(year)1963 7.702049 0.21733283 35.438958 1.382314e-36
factor(year)1964 8.860991 0.19933799 44.452095 2.898574e-41
factor(year)1965 10.976055 0.22396128 49.008719 2.693782e-43
factor(year)1966 15.497606 0.27232979 56.907495 2.015910e-46
factor(year)1967 16.154480 0.22342449 72.303979 1.854232e-51
factor(year)1968 15.901553 0.26908120 59.095740 3.257094e-47
factor(year)1969 19.998661 0.34226735 58.429941 5.631687e-47
factor(year)1970 24.133909 0.35326767 68.316212 2.909474e-50
factor(year)1971 27.006851 0.35609716 75.841243 1.822107e-52
factor(year)1972 29.743890 0.33258138 89.433420 5.987236e-56
factor(year)1973 30.155062 0.21174316 142.413391 8.227494e-66
factor(year)1974 33.856762 0.29513842 114.714859 3.193713e-61
factor(year)1975 35.994452 0.26531167 135.668562 8.814843e-65
factor(year)1976 40.742812 0.37139011 109.703546 2.826387e-60
factor(year)1977 41.875247 0.34252156 122.255801 1.425881e-62
factor(year)1978 41.121062 0.29930528 137.388358 4.762542e-65
factor(year)1979 45.962363 0.48281198 95.197231 2.854883e-57
factor(year)1980 47.315831 0.40972170 115.482854 2.305765e-61
factor(year)1981 50.078337 0.43008897 116.437158 1.542795e-61
factor(year)1982 52.111395 0.48217789 108.075039 5.864289e-60
factor(year)1983 54.729304 0.34709699 157.677264 5.665087e-68
factor(year)1984 57.379535 0.46182636 124.244823 6.482367e-63
factor(year)1985 62.090587 0.41722703 148.817267 9.579938e-67
factor(year)1986 61.547599 0.44985870 136.815400 5.841771e-65
factor(year)1987 67.224168 0.45006855 149.364285 8.006681e-67
factor(year)1988 68.558156 0.48945996 140.068978 1.852053e-65
factor(year)1989 68.754075 0.54962006 125.093824 4.647928e-63
factor(year)1990 74.676405 0.46897719 159.232488 3.505401e-68
factor(year)1991 77.796638 0.55643866 139.811706 2.026192e-65
factor(year)1992 78.613908 0.43968043 178.797831 1.209772e-70
factor(year)1993 81.722417 0.45718676 178.750619 1.225504e-70
factor(year)1994 83.595372 0.64177162 130.257196 6.442630e-64
factor(year)1995 84.478203 0.57809337 146.132454 2.333106e-66
factor(year)1996 90.220939 0.49104702 183.731771 3.193528e-71
factor(year)1997 89.997076 0.50057615 179.786983 9.235805e-71
factor(year)1998 93.969846 0.55747067 168.564646 2.162213e-69
factor(year)1999 97.431784 0.63275009 153.981463 1.806928e-67
factor(year)2000 92.664698 1.35158026 68.560263 2.447364e-50
factor(year)2001 96.955213 1.38393335 70.057718 8.579439e-51
factor(year)2002 99.069756 1.36924761 72.353426 1.793693e-51
factor(year)2003 100.419616 1.45156752 69.180120 1.581560e-50
factor(year)2004 106.697681 1.42879243 74.676824 3.864251e-52
factor(year)2005 107.893435 1.46166275 73.815547 6.788827e-52
factor(year)2006 115.434307 1.04433231 110.534076 1.955981e-60
factor(year)2007 117.506257 1.01363942 115.925106 1.913226e-61
factor(year)2008 122.246112 1.00710633 121.383521 2.022876e-62
factor(year)2009 127.725705 1.02201556 124.974325 4.870072e-63
factor(year)2010 125.573307 1.01903755 123.227360 9.686637e-63
xit 3.054344 0.03832003 79.706198 1.626184e-53
CI Lower CI Upper DF
treatedpost 67.582888 107.067147 49
factor(year)1962 3.406801 4.204952 49
factor(year)1963 7.265302 8.138796 49
factor(year)1964 8.460406 9.261576 49
factor(year)1965 10.525988 11.426122 49
factor(year)1966 14.950339 16.044874 49
factor(year)1967 15.705491 16.603468 49
factor(year)1968 15.360814 16.442292 49
factor(year)1969 19.310849 20.686473 49
factor(year)1970 23.423991 24.843827 49
factor(year)1971 26.291247 27.722455 49
factor(year)1972 29.075543 30.412237 49
factor(year)1973 29.729548 30.580576 49
factor(year)1974 33.263659 34.449865 49
factor(year)1975 35.461288 36.527616 49
factor(year)1976 39.996476 41.489148 49
factor(year)1977 41.186924 42.563570 49
factor(year)1978 40.519585 41.722538 49
factor(year)1979 44.992116 46.932610 49
factor(year)1980 46.492465 48.139198 49
factor(year)1981 49.214041 50.942633 49
factor(year)1982 51.142422 53.080367 49
factor(year)1983 54.031786 55.426821 49
factor(year)1984 56.451460 58.307609 49
factor(year)1985 61.252138 62.929036 49
factor(year)1986 60.643574 62.451624 49
factor(year)1987 66.319721 68.128615 49
factor(year)1988 67.574549 69.541763 49
factor(year)1989 67.649572 69.858578 49
factor(year)1990 73.733960 75.618850 49
factor(year)1991 76.678433 78.914844 49
factor(year)1992 77.730337 79.497479 49
factor(year)1993 80.803665 82.641168 49
factor(year)1994 82.305683 84.885060 49
factor(year)1995 83.316481 85.639925 49
factor(year)1996 89.234144 91.207735 49
factor(year)1997 88.991131 91.003022 49
factor(year)1998 92.849567 95.090126 49
factor(year)1999 96.160225 98.703343 49
factor(year)2000 89.948596 95.380800 49
factor(year)2001 94.174095 99.736331 49
factor(year)2002 96.318150 101.821362 49
factor(year)2003 97.502582 103.336650 49
factor(year)2004 103.826415 109.568947 49
factor(year)2005 104.956114 110.830756 49
factor(year)2006 113.335642 117.532971 49
factor(year)2007 115.469272 119.543242 49
factor(year)2008 120.222256 124.269968 49
factor(year)2009 125.671888 129.779522 49
factor(year)2010 123.525474 127.621140 49
xit 2.977337 3.131351 49did <- round(data.frame(ATT = dta$coefficients["treatedpost"],
se = dta$std.error["treatedpost"],
low_ci = dta$conf.low["treatedpost"],
high_ci = dta$conf.hig["treatedpost"]),2)
did ATT se low_ci high_ci
treatedpost 87.33 9.82 67.58 107.07This quantity above is not giving us the group-specific overall ATT and could also
be biased. It is similar to the simple aggregation when using the did package
Analyze the data: Generalized SCM
y <- gsynth(y ~ treatedpost + xit,
data = mydata,
EM = F,
index = c("state","year"),
inference = "parametric",
se = TRUE,
nboots = 100, #so that it can run faster, default is 200
r = c(0, 5),
CV = TRUE,
seed = 123,
force = "two-way",
parallel = FALSE)Cross-validating ...
r = 0; sigma2 = 0.99374; IC = 0.35642; PC = 0.94548; MSPE = 1.07954*
r = 1; sigma2 = 0.95062; IC = 0.66196; PC = 1.14721; MSPE = 1.09708
r = 2; sigma2 = 0.90559; IC = 0.95480; PC = 1.32515; MSPE = 1.12129
r = 3; sigma2 = 0.87527; IC = 1.25357; PC = 1.50630; MSPE = 1.12952
r = 4; sigma2 = 0.84474; IC = 1.54236; PC = 1.67237; MSPE = 1.15788
r = 5; sigma2 = 0.81465; IC = 1.82186; PC = 1.82457; MSPE = 1.20554
r* = 0
Simulating errors ....Bootstrapping ...
.y1 <- round(data.frame(y$est.avg),2)
#Period-specific ATT
paged_table(y$est.att %>% as.data.frame())#average ATT
y$est.avg Estimate S.E. CI.lower CI.upper p.value
ATT.avg 87.70783 0.1338771 87.44543 87.97022 0plot(y, type = "counterfactual", raw = "none", main="")
plot(y, type = "counterfactual", raw = "band", main="")
plot(y, type = "counterfactual", raw = "all")
plot(y, type = "ct", raw = "none", main = "", shade.post = FALSE)
This quantity above is not giving us the group-specific overall ATT. It is similar to the simple aggregation when using the did package.
Another method is to obtain the overall ATT by estimating the effect in each unit (state) and then pool the estimates as done below
Analyze the data: Pooled effect
Method 1: Estimate single effects and pooled effects using the DID method
## Group 1: "Early adopters", policy enacted in 2000
data_filter_g1 <- mydata %>%
filter(state %in% state.name[1:5] | treated==0)
dta_g1 <- lm_robust(y ~ treatedpost + factor(year) + xit,
data = data_filter_g1,
fixed_effects=state,
clusters = state,
se_type = "stata")
did_g1 <- data.frame(group ="1-Early_adopters",
att = dta_g1$coefficients["treatedpost"],
se = dta_g1$std.error["treatedpost"],
lowerci = dta_g1$conf.low["treatedpost"],
upperci = dta_g1$conf.hig["treatedpost"],
row.names = NULL)
did_g1 group att se lowerci upperci
1 1-Early_adopters 50.31357 0.1348753 50.04076 50.58638##Group 2: "Medium adopters", policy enacted in 2003
data_filter_g2 <- mydata %>%
filter(state %in% state.name[6:10] | treated==0)
dta_g2 <- lm_robust(y ~ treatedpost + factor(year) + xit,
data = data_filter_g2,
fixed_effects=state,
clusters = state,
se_type = "stata")
did_g2 <- data.frame(group = "2-Mid-adopters",
att = dta_g2$coefficients["treatedpost"],
se = dta_g2$std.error["treatedpost"],
lowerci = dta_g2$conf.low["treatedpost"],
upperci = dta_g2$conf.hig["treatedpost"],
row.names = NULL)
did_g2 group att se lowerci upperci
1 2-Mid-adopters 99.91164 0.1877363 99.53191 100.2914##Group 3: "ate adopters", policy enacted in 2006
data_filter_g3 <- mydata %>%
filter(state %in% state.name[11:15] | treated==0)
dta_g3 <- lm_robust(y ~ treatedpost + factor(year) + xit,
data = data_filter_g3,
fixed_effects=state,
clusters = state,
se_type = "stata")
did_g3 <- data.frame(group = "3-Late-adopters",
att = dta_g3$coefficients["treatedpost"],
se = dta_g3$std.error["treatedpost"],
lowerci = dta_g3$conf.low["treatedpost"],
upperci = dta_g3$conf.hig["treatedpost"],
row.names = NULL)
did_g3 group att se lowerci upperci
1 3-Late-adopters 150.359 0.179171 149.9966 150.7214combined <- bind_rows(did_g1, did_g2, did_g3)
combined group att se lowerci upperci
1 1-Early_adopters 50.31357 0.1348753 50.04076 50.58638
2 2-Mid-adopters 99.91164 0.1877363 99.53191 100.29137
3 3-Late-adopters 150.35901 0.1791710 149.99660 150.72142p_load("metafor")
metaresult <- rma(yi = att,
sei = se,
data = combined,
slab = group,
method = "ML")
combined2 <- combined %>%
add_row(group="4-Overall",
att=as.vector(metaresult$beta),
se = metaresult$se,
lowerci = metaresult$ci.lb,
upperci = metaresult$ci.ub)
combined2 group att se lowerci upperci
1 1-Early_adopters 50.31357 0.1348753 50.04076 50.58638
2 2-Mid-adopters 99.91164 0.1877363 99.53191 100.29137
3 3-Late-adopters 150.35901 0.1791710 149.99660 150.72142
4 4-Overall 100.19460 23.5812667 53.97617 146.41303Method 2: Estimate single effects and pooled effects using the generalized SCM
#Create a function to estimate the effect of each state
gsynth_meta <- function(states, data, nboots = 200){
dat <- data %>%
filter(state=={{states}} | treated == 0)
y <- gsynth(y ~ treatedpost + xit,
data = dat,
EM = F,
index = c("state","year"),
inference = "parametric",
se = TRUE,
nboots = nboots,
r = c(0, 5),
CV = TRUE,
seed = 123,
force = "two-way",
parallel = FALSE)
y1 <- data.frame(y$est.avg)
res <- tibble(
state = states,
ATT=y1$Estimate,
SE=y1$S.E.,
lowerci=y1$CI.lower,
upperci=y1$CI.upper)
res
return(res)
}
#test the function
gsynth_meta(states="California",
nboots = 100,
data=mydata)Cross-validating ...
r = 0; sigma2 = 0.99374; IC = 0.35642; PC = 0.94548; MSPE = 1.06914*
r = 1; sigma2 = 0.95062; IC = 0.66196; PC = 1.14721; MSPE = 1.13832
r = 2; sigma2 = 0.90559; IC = 0.95480; PC = 1.32515; MSPE = 1.09489
r = 3; sigma2 = 0.87527; IC = 1.25357; PC = 1.50630; MSPE = 1.06844
r = 4; sigma2 = 0.84474; IC = 1.54236; PC = 1.67237; MSPE = 1.09315
r = 5; sigma2 = 0.81465; IC = 1.82186; PC = 1.82457; MSPE = 1.17677
r* = 0
Simulating errors ....Bootstrapping ...
.# A tibble: 1 × 5
state ATT SE lowerci upperci
<chr> <dbl> <dbl> <dbl> <dbl>
1 California 50.3 0.352 49.6 51.0#create the list and their intersection
states <- mydata %>%
filter(treated == 1) %>%
select(state) %>%
distinct() %>%
as.matrix() %>%
as.vector()
states [1] "Alabama" "Alaska" "Arizona" "Arkansas"
[5] "California" "Colorado" "Connecticut" "Delaware"
[9] "Florida" "Georgia" "Hawaii" "Idaho"
[13] "Illinois" "Indiana" "Iowa" #create list of combinations
list_states <- expand_grid(states) %>% print(n=Inf)# A tibble: 15 × 1
states
<chr>
1 Alabama
2 Alaska
3 Arizona
4 Arkansas
5 California
6 Colorado
7 Connecticut
8 Delaware
9 Florida
10 Georgia
11 Hawaii
12 Idaho
13 Illinois
14 Indiana
15 Iowa list_states# A tibble: 15 × 1
states
<chr>
1 Alabama
2 Alaska
3 Arizona
4 Arkansas
5 California
6 Colorado
7 Connecticut
8 Delaware
9 Florida
10 Georgia
11 Hawaii
12 Idaho
13 Illinois
14 Indiana
15 Iowa #Loop through all the states
gsynth_res <- pmap_dfr(list_states, gsynth_meta, nboots=2, data =mydata)Cross-validating ...
r = 0; sigma2 = 0.99374; IC = 0.35642; PC = 0.94548; MSPE = 0.64418*
r = 1; sigma2 = 0.95062; IC = 0.66196; PC = 1.14721; MSPE = 0.68379
r = 2; sigma2 = 0.90559; IC = 0.95480; PC = 1.32515; MSPE = 0.72328
r = 3; sigma2 = 0.87527; IC = 1.25357; PC = 1.50630; MSPE = 0.75253
r = 4; sigma2 = 0.84474; IC = 1.54236; PC = 1.67237; MSPE = 0.77352
r = 5; sigma2 = 0.81465; IC = 1.82186; PC = 1.82457; MSPE = 0.80889
r* = 0
Simulating errors ...Bootstrapping ...
Cross-validating ...
r = 0; sigma2 = 0.99374; IC = 0.35642; PC = 0.94548; MSPE = 1.26582*
r = 1; sigma2 = 0.95062; IC = 0.66196; PC = 1.14721; MSPE = 1.33597
r = 2; sigma2 = 0.90559; IC = 0.95480; PC = 1.32515; MSPE = 1.41130
r = 3; sigma2 = 0.87527; IC = 1.25357; PC = 1.50630; MSPE = 1.43907
r = 4; sigma2 = 0.84474; IC = 1.54236; PC = 1.67237; MSPE = 1.53695
r = 5; sigma2 = 0.81465; IC = 1.82186; PC = 1.82457; MSPE = 1.53807
r* = 0
Simulating errors ...Bootstrapping ...
Cross-validating ...
r = 0; sigma2 = 0.99374; IC = 0.35642; PC = 0.94548; MSPE = 0.92242*
r = 1; sigma2 = 0.95062; IC = 0.66196; PC = 1.14721; MSPE = 0.93304
r = 2; sigma2 = 0.90559; IC = 0.95480; PC = 1.32515; MSPE = 0.96230
r = 3; sigma2 = 0.87527; IC = 1.25357; PC = 1.50630; MSPE = 1.00950
r = 4; sigma2 = 0.84474; IC = 1.54236; PC = 1.67237; MSPE = 1.04442
r = 5; sigma2 = 0.81465; IC = 1.82186; PC = 1.82457; MSPE = 1.09204
r* = 0
Simulating errors ...Bootstrapping ...
Cross-validating ...
r = 0; sigma2 = 0.99374; IC = 0.35642; PC = 0.94548; MSPE = 0.86174*
r = 1; sigma2 = 0.95062; IC = 0.66196; PC = 1.14721; MSPE = 0.91042
r = 2; sigma2 = 0.90559; IC = 0.95480; PC = 1.32515; MSPE = 0.95074
r = 3; sigma2 = 0.87527; IC = 1.25357; PC = 1.50630; MSPE = 1.02461
r = 4; sigma2 = 0.84474; IC = 1.54236; PC = 1.67237; MSPE = 1.04184
r = 5; sigma2 = 0.81465; IC = 1.82186; PC = 1.82457; MSPE = 1.16591
r* = 0
Simulating errors ...Bootstrapping ...
Cross-validating ...
r = 0; sigma2 = 0.99374; IC = 0.35642; PC = 0.94548; MSPE = 1.06914*
r = 1; sigma2 = 0.95062; IC = 0.66196; PC = 1.14721; MSPE = 1.13832
r = 2; sigma2 = 0.90559; IC = 0.95480; PC = 1.32515; MSPE = 1.09489
r = 3; sigma2 = 0.87527; IC = 1.25357; PC = 1.50630; MSPE = 1.06844
r = 4; sigma2 = 0.84474; IC = 1.54236; PC = 1.67237; MSPE = 1.09315
r = 5; sigma2 = 0.81465; IC = 1.82186; PC = 1.82457; MSPE = 1.17677
r* = 0
Simulating errors ...Bootstrapping ...
Cross-validating ...
r = 0; sigma2 = 0.99374; IC = 0.35642; PC = 0.94548; MSPE = 1.26357*
r = 1; sigma2 = 0.95062; IC = 0.66196; PC = 1.14721; MSPE = 1.31688
r = 2; sigma2 = 0.90559; IC = 0.95480; PC = 1.32515; MSPE = 1.38237
r = 3; sigma2 = 0.87527; IC = 1.25357; PC = 1.50630; MSPE = 1.34588
r = 4; sigma2 = 0.84474; IC = 1.54236; PC = 1.67237; MSPE = 1.38770
r = 5; sigma2 = 0.81465; IC = 1.82186; PC = 1.82457; MSPE = 1.52314
r* = 0
Simulating errors ...Bootstrapping ...
Cross-validating ...
r = 0; sigma2 = 0.99374; IC = 0.35642; PC = 0.94548; MSPE = 1.37702*
r = 1; sigma2 = 0.95062; IC = 0.66196; PC = 1.14721; MSPE = 1.41878
r = 2; sigma2 = 0.90559; IC = 0.95480; PC = 1.32515; MSPE = 1.48540
r = 3; sigma2 = 0.87527; IC = 1.25357; PC = 1.50630; MSPE = 1.43492
r = 4; sigma2 = 0.84474; IC = 1.54236; PC = 1.67237; MSPE = 1.49865
r = 5; sigma2 = 0.81465; IC = 1.82186; PC = 1.82457; MSPE = 1.51816
r* = 0
Simulating errors ...Bootstrapping ...
Cross-validating ...
r = 0; sigma2 = 0.99374; IC = 0.35642; PC = 0.94548; MSPE = 1.00858
r = 1; sigma2 = 0.95062; IC = 0.66196; PC = 1.14721; MSPE = 0.94450*
r = 2; sigma2 = 0.90559; IC = 0.95480; PC = 1.32515; MSPE = 0.96840
r = 3; sigma2 = 0.87527; IC = 1.25357; PC = 1.50630; MSPE = 0.89605*
r = 4; sigma2 = 0.84474; IC = 1.54236; PC = 1.67237; MSPE = 0.93885
r = 5; sigma2 = 0.81465; IC = 1.82186; PC = 1.82457; MSPE = 0.98340
r* = 3
Simulating errors ...Bootstrapping ...
Cross-validating ...
r = 0; sigma2 = 0.99374; IC = 0.35642; PC = 0.94548; MSPE = 1.23771*
r = 1; sigma2 = 0.95062; IC = 0.66196; PC = 1.14721; MSPE = 1.25700
r = 2; sigma2 = 0.90559; IC = 0.95480; PC = 1.32515; MSPE = 1.29435
r = 3; sigma2 = 0.87527; IC = 1.25357; PC = 1.50630; MSPE = 1.36304
r = 4; sigma2 = 0.84474; IC = 1.54236; PC = 1.67237; MSPE = 1.20627*
r = 5; sigma2 = 0.81465; IC = 1.82186; PC = 1.82457; MSPE = 1.29253
r* = 4
Simulating errors ...Bootstrapping ...
Cross-validating ...
r = 0; sigma2 = 0.99374; IC = 0.35642; PC = 0.94548; MSPE = 1.44689
r = 1; sigma2 = 0.95062; IC = 0.66196; PC = 1.14721; MSPE = 1.38143*
r = 2; sigma2 = 0.90559; IC = 0.95480; PC = 1.32515; MSPE = 1.41352
r = 3; sigma2 = 0.87527; IC = 1.25357; PC = 1.50630; MSPE = 1.41338
r = 4; sigma2 = 0.84474; IC = 1.54236; PC = 1.67237; MSPE = 1.50633
r = 5; sigma2 = 0.81465; IC = 1.82186; PC = 1.82457; MSPE = 1.49618
r* = 1
Simulating errors ...Bootstrapping ...
Cross-validating ...
r = 0; sigma2 = 0.99374; IC = 0.35642; PC = 0.94548; MSPE = 0.98163*
r = 1; sigma2 = 0.95062; IC = 0.66196; PC = 1.14721; MSPE = 0.99824
r = 2; sigma2 = 0.90559; IC = 0.95480; PC = 1.32515; MSPE = 0.90518*
r = 3; sigma2 = 0.87527; IC = 1.25357; PC = 1.50630; MSPE = 0.94306
r = 4; sigma2 = 0.84474; IC = 1.54236; PC = 1.67237; MSPE = 0.93514
r = 5; sigma2 = 0.81465; IC = 1.82186; PC = 1.82457; MSPE = 0.97587
r* = 2
Simulating errors ...Bootstrapping ...
Cross-validating ...
r = 0; sigma2 = 0.99374; IC = 0.35642; PC = 0.94548; MSPE = 1.15811*
r = 1; sigma2 = 0.95062; IC = 0.66196; PC = 1.14721; MSPE = 1.19254
r = 2; sigma2 = 0.90559; IC = 0.95480; PC = 1.32515; MSPE = 1.20397
r = 3; sigma2 = 0.87527; IC = 1.25357; PC = 1.50630; MSPE = 1.26865
r = 4; sigma2 = 0.84474; IC = 1.54236; PC = 1.67237; MSPE = 1.32133
r = 5; sigma2 = 0.81465; IC = 1.82186; PC = 1.82457; MSPE = 1.35986
r* = 0
Simulating errors ...Bootstrapping ...
Cross-validating ...
r = 0; sigma2 = 0.99374; IC = 0.35642; PC = 0.94548; MSPE = 0.99759
r = 1; sigma2 = 0.95062; IC = 0.66196; PC = 1.14721; MSPE = 0.97693*
r = 2; sigma2 = 0.90559; IC = 0.95480; PC = 1.32515; MSPE = 1.00514
r = 3; sigma2 = 0.87527; IC = 1.25357; PC = 1.50630; MSPE = 1.05921
r = 4; sigma2 = 0.84474; IC = 1.54236; PC = 1.67237; MSPE = 1.08999
r = 5; sigma2 = 0.81465; IC = 1.82186; PC = 1.82457; MSPE = 1.14096
r* = 1
Simulating errors ...Bootstrapping ...
Cross-validating ...
r = 0; sigma2 = 0.99374; IC = 0.35642; PC = 0.94548; MSPE = 1.18469*
r = 1; sigma2 = 0.95062; IC = 0.66196; PC = 1.14721; MSPE = 1.18814
r = 2; sigma2 = 0.90559; IC = 0.95480; PC = 1.32515; MSPE = 1.23547
r = 3; sigma2 = 0.87527; IC = 1.25357; PC = 1.50630; MSPE = 1.12889*
r = 4; sigma2 = 0.84474; IC = 1.54236; PC = 1.67237; MSPE = 1.18063
r = 5; sigma2 = 0.81465; IC = 1.82186; PC = 1.82457; MSPE = 1.16628
r* = 3
Simulating errors ...Bootstrapping ...
Cross-validating ...
r = 0; sigma2 = 0.99374; IC = 0.35642; PC = 0.94548; MSPE = 0.75178*
r = 1; sigma2 = 0.95062; IC = 0.66196; PC = 1.14721; MSPE = 0.77123
r = 2; sigma2 = 0.90559; IC = 0.95480; PC = 1.32515; MSPE = 0.78375
r = 3; sigma2 = 0.87527; IC = 1.25357; PC = 1.50630; MSPE = 0.80219
r = 4; sigma2 = 0.84474; IC = 1.54236; PC = 1.67237; MSPE = 0.82338
r = 5; sigma2 = 0.81465; IC = 1.82186; PC = 1.82457; MSPE = 0.86463
r* = 0
Simulating errors ...Bootstrapping ...
#used nboots = 2 for simplicity. You should aim for at least 200 bootstrap samples
gsynth_res# A tibble: 15 × 5
state ATT SE lowerci upperci
<chr> <dbl> <dbl> <dbl> <dbl>
1 Alabama 49.9 0.157 49.6 50.2
2 Alaska 50.4 0.0863 50.2 50.5
3 Arizona 50.1 0.123 49.9 50.4
4 Arkansas 50.8 0.136 50.6 51.1
5 California 50.3 0.112 50.0 50.5
6 Colorado 100. 0.163 100. 101.
7 Connecticut 100.0 0.164 99.7 100.
8 Delaware 99.3 0.285 98.8 99.9
9 Florida 100.0 0.0171 99.9 100.
10 Georgia 99.7 0.172 99.4 100.
11 Hawaii 150. 0.323 150. 151.
12 Idaho 150. 0.357 149. 151.
13 Illinois 150. 0.234 150. 151.
14 Indiana 151. 0.406 150. 152.
15 Iowa 150. 0.361 149. 151. #estimate the overall effect
metaresult2 <- rma(yi = ATT,
sei = SE,
data = gsynth_res,
slab = state,
method = "ML")
#combine the datasets
combined3 <- gsynth_res %>%
add_row(state="Overall",
ATT=as.vector(metaresult2$beta),
SE = metaresult2$se,
lowerci = metaresult2$ci.lb,
upperci = metaresult2$ci.ub)
combined3# A tibble: 16 × 5
state ATT SE lowerci upperci
<chr> <dbl> <dbl> <dbl> <dbl>
1 Alabama 49.9 0.157 49.6 50.2
2 Alaska 50.4 0.0863 50.2 50.5
3 Arizona 50.1 0.123 49.9 50.4
4 Arkansas 50.8 0.136 50.6 51.1
5 California 50.3 0.112 50.0 50.5
6 Colorado 100. 0.163 100. 101.
7 Connecticut 100.0 0.164 99.7 100.
8 Delaware 99.3 0.285 98.8 99.9
9 Florida 100.0 0.0171 99.9 100.
10 Georgia 99.7 0.172 99.4 100.
11 Hawaii 150. 0.323 150. 151.
12 Idaho 150. 0.357 149. 151.
13 Illinois 150. 0.234 150. 151.
14 Indiana 151. 0.406 150. 152.
15 Iowa 150. 0.361 149. 151.
16 Overall 100. 10.5 79.5 121. #note that this has better standard errors compared to when pooling the
#three groupsMethod 3: Estimate single effects and pooled effects using the Augmented Synthetic Control Methods
p_load_gh("ebenmichael/augsynth")
set.seed(123)
augsynth.scm <-multisynth(y ~ treatedpost | xit,
unit = state,
time = year,
data = mydata,
fixedeff = T,
scm = T,
time_cohort = F, #can change this to T if interested in time cohorts instead of unit effects
progfunc="Ridge")
#progfunc = None for Traditional SCM,
#progfunc = Ridge for Ridge regression or augmented SCM
#progfunc = GSYN for the Generalized SCM
augsynth.scm
Call:
multisynth(form = y ~ treatedpost | xit, unit = state, time = year,
data = mydata, fixedeff = T, scm = T, time_cohort = F, progfunc = "Ridge")
Average ATT Estimate: 100.135res <- summary(augsynth.scm)
res
Call:
multisynth(form = y ~ treatedpost | xit, unit = state, time = year,
data = mydata, fixedeff = T, scm = T, time_cohort = F, progfunc = "Ridge")
Average ATT Estimate (Std. Error): 100.135 (72.068)
Global L2 Imbalance: 0.918
Scaled Global L2 Imbalance: 0.010
Percent improvement from uniform global weights: 99
Individual L2 Imbalance: 12.667
Scaled Individual L2 Imbalance: 0.116
Percent improvement from uniform individual weights: 88.4
Time Since Treatment Level Estimate Std.Error lower_bound
0 Average 100.64440 69.77992 -11.828943
1 Average 100.29211 74.15262 -17.224750
2 Average 99.69507 72.99860 -16.226749
3 Average 99.83579 65.01774 -9.376898
4 Average 100.20612 79.52793 -23.752444
upper_bound
244.3967
250.5294
247.1873
232.4593
260.1102paged_table(res$att)#is a dataframe that contains all of the point estimates, standard errors, and lower/upper confidence limits. Time = NA denotes the effect averaged across the post treatment periods.
plot(augsynth.scm)Joining with `by = join_by(Level)`Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none"
instead as of ggplot2 3.3.4.
ℹ The deprecated feature was likely used in the augsynth package.
Please report the issue to the authors.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning
was generated.Warning: Removed 61 rows containing missing values or values outside the scale
range (`geom_line()`).Warning: Removed 61 rows containing missing values or values outside the scale
range (`geom_point()`).
plot(augsynth.scm, levels = "Average")Joining with `by = join_by(Level)`Warning: Removed 1 row containing missing values or values outside the scale
range (`geom_line()`).Warning: Removed 1 row containing missing values or values outside the scale
range (`geom_point()`).